U.S. patent application number 13/243135 was filed with the patent office on 2012-01-12 for statistical processing methods used in abnormal situation detection.
This patent application is currently assigned to FISHER-ROSEMOUNT SYSTEMS, INC.. Invention is credited to Kadir Kavaklioglu.
Application Number | 20120011180 13/243135 |
Document ID | / |
Family ID | 36693113 |
Filed Date | 2012-01-12 |
United States Patent
Application |
20120011180 |
Kind Code |
A1 |
Kavaklioglu; Kadir |
January 12, 2012 |
STATISTICAL PROCESSING METHODS USED IN ABNORMAL SITUATION
DETECTION
Abstract
Detection of one or more abnormal situations is performed using
various statistical measures, such as a mean, a median, a standard
deviation, etc. of one or more process parameters or variable
measurements made by statistical process monitoring blocks within a
plant. This detection is enhanced in various cases by using
specialized data filters and data processing techniques, which are
designed to be computationally simple and therefore are able to be
applied to data collected at a high sampling rate in a field device
having limited processing power. The enhanced data or measurements
may be used to provided better or more accurate statistical
measures of the data, may be used to trim the data to remove
outliers from this data, may be used to fit this data to non-linear
functions, or may be use to quickly detect the occurrence of
various abnormal situations within specific plant equipment, such
as distillation columns and fluid catalytic crackers.
Inventors: |
Kavaklioglu; Kadir; (Eden
Prairie, MN) |
Assignee: |
FISHER-ROSEMOUNT SYSTEMS,
INC.
AUSTIN
TX
|
Family ID: |
36693113 |
Appl. No.: |
13/243135 |
Filed: |
September 23, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12793425 |
Jun 3, 2010 |
8027804 |
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13243135 |
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10589728 |
Aug 17, 2006 |
7752012 |
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PCT/US06/12445 |
Apr 4, 2006 |
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12793425 |
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60668243 |
Apr 4, 2005 |
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Current U.S.
Class: |
708/202 ;
708/400 |
Current CPC
Class: |
G05B 23/0221 20130101;
C10G 11/187 20130101; G05B 23/024 20130101 |
Class at
Publication: |
708/202 ;
708/400 |
International
Class: |
G06F 17/14 20060101
G06F017/14 |
Claims
1. A method of fitting a sine wave to data collected within a
process plant, comprising: determining a first set of parameters of
the sine wave based on one or more statistical measures of the
process parameter determined from the data collected within the
process plant; storing a variable transformation of a mathematical
expression of the sine wave that produces a linear expression
having a second set of sine wave parameters associated therewith;
using the variable transformation to produce a set of transformed
data points from the data collected within the process plant;
performing a linear regression to fit the transformed data points
to the linear expression; and determining the second set of sine
wave parameters based on the linear regression.
2. The method of claim 1, wherein the first set of parameters of
the sine wave includes an offset and a gain.
3. The method of claim 2, wherein determining the first set of
parameters of the sine wave includes determining the offset as a
mean value of the data collected within the process plant and
determining the gain based on the difference between a minimum
value and a maximum value of the data collected within the process
plant.
4. The method of claim 2, wherein the second set of parameters of
the sine wave includes a cyclic frequency and a phase.
5. The method of claim 2, wherein the variable transformation is of
the form: z = Sin - 1 ( y ) - a b ##EQU00005## wherein: z is a
transformed data point; y is a collected data point; a is the
offset; and b is the gain, and wherein the linear expression is of
the form: z(t)=.omega.t+.phi. wherein: z(t) is the transformed data
point a time t; .omega. is a periodic frequency; and .phi. is a
phase.
6. The method of claim 5, further including applying a variable
transformation to produce a further linear expression including the
offset and the gain, applying a linear regression to the further
linear expression to determine a new set of values for the offset
and the gain and determining a new set of values for the periodic
frequency and the phase based on the new set of values for the
offset and the gain.
7. The method of claim 6, including iteratively determining values
for the sine wave offset, gain, periodic frequency and phase until
a change in the values for one or more of the sine wave offset,
gain, periodic frequency and phase becomes less than one or more
threshold values.
8. The method of claim 1, wherein determining the first set of
parameters of the sine wave, using the variable transformation,
performing the linear regression and determining the second set of
sine wave parameters are performed in a device that collects or
measures the data collected within the process plant.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional application of and claims
priority to prior U.S. patent application Ser. No. 12/793,425,
entitled "Statistical Processing Methods Used in Abnormal Situation
Detection," which was filed Jun. 3, 2010, which is a divisional
application of and claims priority to prior U.S. patent application
Ser. No. 10/589,728, entitled "Statistical Processing Methods Used
in Abnormal Situation Detection," which was filed on Aug. 17, 2006,
which is a U.S. national stage of PCT/US06/12445, entitled
"Statistical Processing Methods Used in Abnormal Situation
Detection," which was filed Apr. 4, 2006, which in turn claims the
benefit of U.S. Provisional Application No. 60/668,243 entitled
"Process Diagnostics," which was filed on Apr. 4, 2005, all of
which are hereby expressly incorporated by reference herein in
their entirety for all purposes.
FIELD OF THE DISCLOSURE
[0002] This patent relates generally to performing diagnostics and
maintenance in a process plant and, more particularly, to providing
diagnostic capabilities within a process plant in a manner that
reduces or prevents abnormal situations within the process
plant.
BACKGROUND
[0003] Process control systems, like those used in chemical,
petroleum or other processes, typically include one or more
centralized or decentralized process controllers communicatively
coupled to at least one host or operator workstation and to one or
more process control and instrumentation devices such as, for
example, field devices, via analog, digital or combined
analog/digital buses. Field devices, which may be, for example,
valves, valve positioners, switches, transmitters, and sensors
(e.g., temperature, pressure, and flow rate sensors), are located
within the process plant environment, and perform functions within
the process such as opening or closing valves, measuring process
parameters, increasing or decreasing fluid flow, etc. Smart field
devices such as field devices conforming to the well-known
FOUNDATION.TM. Fieldbus (hereinafter "Fieldbus") protocol or the
HART.RTM. protocol may also perform control calculations, alarming
functions, and other control functions commonly implemented within
the process controller.
[0004] The process controllers, which are typically located within
the process plant environment, receive signals indicative of
process measurements or process variables made by or associated
with the field devices and/or other information pertaining to the
field devices, and execute controller applications. The controller
applications implement, for example, different control modules that
make process control decisions, generate control signals based on
the received information, and coordinate with the control modules
or blocks being performed in the field devices such as HART and
Fieldbus field devices. The control modules in the process
controllers send the control signals over the communication lines
or signal paths to the field devices, to thereby control the
operation of the process.
[0005] Information from the field devices and the process
controllers is typically made available to one or more other
hardware devices such as, for example, operator workstations,
maintenance workstations, personal computers, handheld devices,
data historians, report generators, centralized databases, etc. to
enable an operator or a maintenance person to perform desired
functions with respect to the process such as, for example,
changing settings of the process control routine, modifying the
operation of the control modules within the process controllers or
the smart field devices, viewing the current state of the process
or of particular devices within the process plant, viewing alarms
generated by field devices and process controllers, simulating the
operation of the process for the purpose of training personnel or
testing the process control software, diagnosing problems or
hardware failures within the process plant, etc.
[0006] While a typical process plant has many process control and
instrumentation devices such as valves, transmitters, sensors, etc.
connected to one or more process controllers, there are many other
supporting devices that are also necessary for or related to
process operation. These additional devices include, for example,
power supply equipment, power generation and distribution
equipment, rotating equipment such as turbines, motors, etc., which
are located at numerous places in a typical plant. While this
additional equipment does not necessarily create or use process
variables and, in many instances, is not controlled or even coupled
to a process controller for the purpose of affecting the process
operation, this equipment is nevertheless important to, and
ultimately necessary for proper operation of the process.
[0007] As is known, problems frequently arise within a process
plant environment, especially a process plant having a large number
of field devices and supporting equipment. These problems may take
the form of broken or malfunctioning devices, plugged fluid lines
or pipes, logic elements, such as software routines, being
improperly configured or being in improper modes, process control
loops being improperly tuned, one or more failures in
communications between devices within the process plant, etc. These
and other problems, while numerous in nature, generally result in
the process operating in an abnormal state (i.e., the process plant
being in an abnormal situation) which is usually associated with
suboptimal performance of the process plant. Many diagnostic tools
and applications have been developed to detect and determine the
cause of problems within a process plant and to assist an operator
or a maintenance person to diagnose and correct the problems, once
the problems have occurred and been detected. For example, operator
workstations, which are typically connected to the process
controllers through communication connections such as a direct or a
wireless bus, an Ethernet, a modem, a phone line, and the like,
have processors and memories that are adapted to run software or
firmware, such as the DeltaV.TM. and Ovation control systems, sold
by Emerson Process Management, wherein the software includes
numerous control module and control loop diagnostic tools.
Likewise, maintenance workstations, which may be connected to the
process control devices, such as field devices, via the same
communication connections as the controller applications, or via
different communication connections, such as OPC connections,
handheld connections, etc., typically include one or more
applications designed to view maintenance alarms and alerts
generated by field devices within the process plant, to test
devices within the process plant and to perform maintenance
activities on the field devices and other devices within the
process plant. Similar diagnostic applications have been developed
to diagnose problems within the supporting equipment within the
process plant.
[0008] Thus, for example, the Asset Management Solutions (AMS)
application (at least partially disclosed in U.S. Pat. No.
5,960,214 entitled "Integrated Communication Network for use in a
Field Device Management System") sold by Emerson Process
Management, enables communication with and stores data pertaining
to field devices to ascertain and track the operating state of the
field devices. In some instances, the AMS application may be used
to communicate with a field device to change parameters within the
field device, to cause the field device to run applications on
itself such as, for example, self-calibration routines or
self-diagnostic routines, to obtain information about the status or
health of the field device, etc. This information may include, for
example, status information (e.g., whether an alarm or other
similar event has occurred), device configuration information
(e.g., the manner in which the field device is currently or may be
configured and the type of measuring units used by the field
device), device parameters (e.g., the field device range values and
other parameters), etc. Of course, this information may be used by
a maintenance person to monitor, maintain, and/or diagnose problems
with field devices.
[0009] Similarly, many process plants include equipment monitoring
and diagnostic applications such as, for example, RBMware provided
by CSI Systems, or any other known applications used to monitor,
diagnose, and optimize the operating state of various rotating
equipment. Maintenance personnel usually use these applications to
maintain and oversee the performance of rotating equipment in the
plant, to determine problems with the rotating equipment, and to
determine when and if the rotating equipment must be repaired or
replaced. Similarly, many process plants include power control and
diagnostic applications such as those provided by, for example, the
Liebert and ASCO companies, to control and maintain the power
generation and distribution equipment. It is also known to run
control optimization applications such as, for example, real-time
optimizers (RTO+), within a process plant to optimize the control
activities of the process plant. Such optimization applications
typically use complex algorithms and/or models of the process plant
to predict how inputs may be changed to optimize operation of the
process plant with respect to some desired optimization variable
such as, for example, profit.
[0010] These and other diagnostic and optimization applications are
typically implemented on a system-wide basis in one or more of the
operator or maintenance workstations, and may provide preconfigured
displays to the operator or maintenance personnel regarding the
operating state of the process plant, or the devices and equipment
within the process plant. Typical displays include alarming
displays that receive alarms generated by the process controllers
or other devices within the process plant, control displays
indicating the operating state of the process controllers and other
devices within the process plant, maintenance displays indicating
the operating state of the devices within the process plant, etc.
Likewise, these and other diagnostic applications may enable an
operator or a maintenance person to retune a control loop or to
reset other control parameters, to run a test on one or more field
devices to determine the current status of those field devices, to
calibrate field devices or other equipment, or to perform other
problem detection and correction activities on devices and
equipment within the process plant.
[0011] While these various applications and tools are very helpful
in identifying and correcting problems within a process plant,
these diagnostic applications are generally configured to be used
only after a problem has already occurred within a process plant
and, therefore, after an abnormal situation already exists within
the plant. Unfortunately, an abnormal situation may exist for some
time before it is detected, identified and corrected using these
tools, resulting in the suboptimal performance of the process plant
for the period of time during which the problem is detected,
identified and corrected. In many cases, a control operator will
first detect that some problem exists based on alarms, alerts or
poor performance of the process plant. The operator will then
notify the maintenance personnel of the potential problem. The
maintenance personnel may or may not detect an actual problem and
may need further prompting before actually running tests or other
diagnostic applications, or performing other activities needed to
identify the actual problem. Once the problem is identified, the
maintenance personnel may need to order parts and schedule a
maintenance procedure, all of which may result in a significant
period of time between the occurrence of a problem and the
correction of that problem, during which time the process plant
runs in an abnormal situation generally associated with the
sub-optimal operation of the plant.
[0012] Additionally, many process plants can experience an abnormal
situation which results in significant costs or damage within the
plant in a relatively short amount of time. For example, some
abnormal situations can cause significant damage to equipment, the
loss of raw materials, or significant unexpected downtime within
the process plant if these abnormal situations exist for even a
short amount of time. Thus, merely detecting a problem within the
plant after the problem has occurred, no matter how quickly the
problem is corrected, may still result in significant loss or
damage within the process plant. As a result, it is desirable to
try to prevent abnormal situations from arising in the first place,
instead of simply trying to react to and correct problems within
the process plant after an abnormal situation arises.
[0013] There is currently one technique that may be used to collect
data that enables a user to predict the occurrence of certain
abnormal situations within a process plant before these abnormal
situations actually arise or shortly after they arise, with the
purpose of taking steps to prevent the predicted abnormal situation
or to correct the abnormal situation before any significant loss
within the process plant takes place. This procedure is disclosed
in U.S. patent application Ser. No. 09/972,078, entitled "Root
Cause Diagnostics" (based in part on U.S. patent application Ser.
No. 08/623,569, now U.S. Pat. No. 6,017,143). The entire
disclosures of both of these applications/patents are hereby
incorporated by reference herein. Generally speaking, this
technique places statistical data collection and processing blocks
or statistical processing monitoring (SPM) blocks, in each of a
number of devices, such as field devices, within a process plant.
The statistical data collection and processing blocks collect, for
example, process variable data and determine certain statistical
measures associated with the collected data, such as a mean, a
median, a standard deviation, etc. These statistical measures may
then sent to a user interface or other processing device and
analyzed to recognize patterns suggesting the actual or future
occurrence of a known abnormal situation. Once a particular
suspected abnormal situation is detected, steps may be taken to
correct the underlying problem, thereby avoiding the abnormal
situation in the first place or correcting the abnormal situation
quickly. However, the collection and analysis of this data may be
time consuming and tedious for a typical maintenance operator,
especially in process plants having a large number of field devices
collecting this statistical data. Still further, while a
maintenance person may be able to collect the statistical data,
this person may not know how to best analyze or view the data or to
determine what, if any, future abnormal situation may be suggested
by the data.
SUMMARY
[0014] Detection or prediction of one or more abnormal situations
is performed using various statistical measures, such as a mean,
median, standard deviation, etc. of process parameters or variable
measurements determined by statistical process monitoring (SPM)
blocks within a plant. This detection is enhanced in various cases
by the use of specialized data filters and data processing
techniques, which are designed to be computationally simple and
therefore are able to be applied to data collected at a high
sampling rate in a field device having limited processing power.
The enhanced data or measurements may be used to provided better or
more accurate statistical measures of the process variable or
process parameter, may be used to trim the data to remove outliers
from this data, may be used to fit this data to non-linear
functions, or may be use to quickly detect the occurrence of
various abnormal situations within specific plant equipment, such
as distillation columns and refinery catalytic crackers. While the
statistical data collection and processing and abnormal situation
detection may be performed within a user interface device or other
maintenance device within a process plant, these methods may also
and advantageously be used in the devices, such as field devices
like valves, transmitters, etc. which collect the data in the first
place, thereby removing the processing burden from the centralized
user interface device as well as the communication overhead
associated with sending the statistical data from the field devices
to the user interface device.
[0015] The methods described herein can be applied in many
different scenarios within a process plant on many different types
of data, to detect whether one or more abnormal situations exist or
may be developing within a plant. For example, the statistical data
may comprise statistical data generated based on pressure, level,
flow, position and temperature variables sensed by one or more
pressure, level, flow, position and temperature sensors associated
with, for example, a distillation column or a refinery catalytic
cracker unit. Of course, if an abnormal situation is detected, an
indicator of the abnormal situation may be generated and the
indicator may be used, for example, to notify an operator or
maintenance personnel or to affect control of plant equipment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is an exemplary block diagram of a process plant
having a distributed control and maintenance network including one
or more operator and maintenance workstations, controllers, field
devices and supporting equipment;
[0017] FIG. 2 is an exemplary block diagram of a portion of the
process plant of FIG. 1, illustrating communication
interconnections between various components of an abnormal
situation prevention system located within different elements of
the process plant, including the use of statistical process
monitoring (SPM) blocks;
[0018] FIG. 3 is a block diagram of an example SPM block;
[0019] FIG. 4 is a display illustrating the configuration of a set
of statistical process monitoring blocks within a device of the
process plant of FIG. 1 or 2;
[0020] FIG. 5 is a block diagram of an example SPM module that uses
multiple SPM blocks and a data processing block to perform signal
processing on raw data to produce enhanced SPM statistics;
[0021] FIG. 6 is a block diagram of a first example data processing
block of FIG. 5 that implements one of multiple different types of
filters;
[0022] FIG. 7 is a block diagram of a second example data
processing block of FIG. 5 that includes data trimming blocks and
that implements one or more different types of filters to produce
filtered and trimmed data;
[0023] FIG. 8 illustrates the transfer function of a known
16.sup.th order FIR high pass filter.
[0024] FIG. 9 illustrates a transfer function of a difference
filter that may be used to filter received process data in an SPM
module;
[0025] FIG. 10 illustrates a set of raw pressure data including
process noise and transients to which the filter of FIG. 9 is to be
applied;
[0026] FIG. 11 illustrates a set of filtered data after application
of the filter of FIG. 9 on the pressure data of FIG. 10;
[0027] FIG. 12 illustrates a plot of a typical pressure signal in
the time domain;
[0028] FIG. 13 illustrates a frequency domain representation of the
pressure signal of FIG. 12 after the application of a Fast Fourier
Transform;
[0029] FIG. 14 is a block diagram of a typical distillation column
used in refineries and chemical plants;
[0030] FIG. 15 is a block diagram illustrating various trays of the
fractionator of the distillation column of FIG. 14; and
[0031] FIG. 16 is a block diagram of a typical fluid catalytic
cracker used in a refinery.
DETAILED DESCRIPTION
[0032] Referring now to FIG. 1, an example process plant 10 in
which an abnormal situation prevention system may be implemented
includes a number of control and maintenance systems interconnected
together with supporting equipment via one or more communication
networks. In particular, the process plant 10 of FIG. 1 includes
one or more process control systems 12 and 14. The process control
system 12 may be a traditional process control system such as a
PROVOX or RS3 system or any other control system which includes an
operator interface 12A coupled to a controller 12B and to
input/output (I/O) cards 12C which, in turn, are coupled to various
field devices such as analog and Highway Addressable Remote
Transmitter (HART) field devices 15. The process control system 14,
which may be a distributed process control system, includes one or
more operator interfaces 14A coupled to one or more distributed
controllers 14B via a bus, such as an Ethernet bus. The controllers
14B may be, for example, DeltaV.TM. controllers sold by Emerson
Process Management of Austin, Tex. or any other desired type of
controllers. The controllers 14B are connected via I/O devices to
one or more field devices 16, such as for example, HART or Fieldbus
field devices or any other smart or non-smart field devices
including, for example, those that use any of the PROFIBUS.RTM.,
WORLDFIP.RTM., Device-Net.RTM., AS-Interface and CAN protocols. As
is known, the field devices 16 may provide analog or digital
information to the controllers 14B related to process variables as
well as to other device information. The operator interfaces 14A
may store and execute tools available to the process control
operator for controlling the operation of the process including,
for example, control optimizers, diagnostic experts, neural
networks, tuners, etc.
[0033] Still further, maintenance systems, such as computers
executing the AMS application or any other device monitoring and
communication applications may be connected to the process control
systems 12 and 14 or to the individual devices therein to perform
maintenance and monitoring activities. For example, a maintenance
computer 18 may be connected to the controller 12B and/or to the
devices 15 via any desired communication lines or networks
(including wireless or handheld device networks) to communicate
with and, in some instances, to reconfigure or to perform other
maintenance activities on the devices 15. Similarly, maintenance
applications such as the AMS application may be installed in and
executed by one or more of the user interfaces 14A associated with
the distributed process control system 14 to perform maintenance
and monitoring functions, including data collection related to the
operating status of the devices 16.
[0034] The process plant 10 also includes various rotating
equipment 20, such as turbines, motors, etc. which are connected to
a maintenance computer 22 via some permanent or temporary
communication link (such as a bus, a wireless communication system
or hand held devices which are connected to the equipment 20 to
take readings and are then removed). The maintenance computer 22
may store and execute known monitoring and diagnostic applications
23 provided by, for example, CSI (an Emerson Process Management
Company) or other any other known applications used to diagnose,
monitor and optimize the operating state of the rotating equipment
20. Maintenance personnel usually use the applications 23 to
maintain and oversee the performance of rotating equipment 20 in
the plant 10, to determine problems with the rotating equipment 20
and to determine when and if the rotating equipment 20 must be
repaired or replaced. In some cases, outside consultants or service
organizations may temporarily acquire or measure data pertaining to
the equipment 20 and use this data to perform analyses for the
equipment 20 to detect problems, poor performance or other issues
effecting the equipment 20. In these cases, the computers running
the analyses may not be connected to the rest of the system 10 via
any communication line or may be connected only temporarily.
[0035] Similarly, a power generation and distribution system 24
having power generating and distribution equipment 25 associated
with the plant 10 is connected via, for example, a bus, to another
computer 26 which runs and oversees the operation of the power
generating and distribution equipment 25 within the plant 10. The
computer 26 may execute known power control and diagnostics
applications 27 such a as those provided by, for example, Liebert
and ASCO or other companies to control and maintain the power
generation and distribution equipment 25. Again, in many cases,
outside consultants or service organizations may use service
applications that temporarily acquire or measure data pertaining to
the equipment 25 and use this data to perform analyses for the
equipment 25 to detect problems, poor performance or other issues
effecting the equipment 25. In these cases, the computers (such as
the computer 26) running the analyses may not be connected to the
rest of the system 10 via any communication line or may be
connected only temporarily.
[0036] As illustrated in FIG. 1, a computer system 30 implements at
least a portion of an abnormal situation prevention system 35, and
in particular, the computer system 30 stores and implements a
configuration and data collection application 38, a viewing or
interface application 40, which may include statistical collection
and processing blocks, and a rules engine development and execution
application 42 and, additionally, stores a statistical process
monitoring database 43 that stores statistical data generated
within certain devices within the process, such as statistical
measures of various process parameters. Generally speaking, the
configuration and data collection application 38 configures and
communicates with each of a number of statistical data collection
and analysis blocks (not shown in FIG. 1) located in the field
devices 15, 16, the controllers 12B, 14B, the rotating equipment 20
or its supporting computer 22, the power generation equipment 25 or
its supporting computer 26 and any other desired devices and
equipment within the process plant 10, to thereby collect
statistical data (or in some cases, actual raw process variable
data) from each of these blocks with which to perform abnormal
situation detection. The configuration and data collection
application 38 may be communicatively connected via a hardwired bus
45 to each of the computers or devices within the plant 10 or,
alternatively, may be connected via any other desired communication
connection including, for example, wireless connections, dedicated
connections which use OPC, intermittent connections, such as ones
which rely on handheld devices to collect data, etc.
[0037] Likewise, the application 38 may obtain data pertaining to
the field devices and equipment within the process plant 10 via a
LAN or a public connection, such as the Internet, a telephone
connection, etc. (illustrated in FIG. 1 as an Internet connection
46) with such data being collected by, for example, a third party
service provider. Further, the application 38 may be
communicatively coupled to computers/devices in the plant 10 via a
variety of techniques and/or protocols including, for example,
Ethernet, Modbus, HTML, XML, proprietary techniques/protocols, etc.
Thus, although particular examples using OPC to communicatively
couple the application 38 to computers/devices in the plant 10 are
described herein, one of ordinary skill in the art will recognize
that a variety of other methods of coupling the application 38 to
computers/devices in the plant 10 can be used as well. The
application 38 may generally store the collected data in the
database 43.
[0038] Once the statistical data (or process variable data) is
collected, the viewing application 40 may be used to process this
data and/or to display the collected or processed statistical data
(e.g., as stored in the database 43) in different manners to enable
a user, such as a maintenance person, to better be able to
determine the existence of or the predicted future existence of an
abnormal situation and to take preemptive or actual corrective
actions. The rules engine development and execution application 42
may use one or more rules stored therein to analyze the collected
data to determine the existence of, or to predict the future
existence of an abnormal situation within the process plant 10.
Additionally, the rules engine development and execution
application 42 may enable an operator or other user to create
additional rules to be implemented by a rules engine to detect or
predict abnormal situations. It is appreciated that the detection
of an abnormal situation as described herein encompasses the
prediction of a future occurrence of an abnormal situation.
[0039] FIG. 2 illustrates a portion 50 of the example process plant
10 of FIG. 1 for the purpose of describing one manner in which
statistical data collection and processing and in some cases
abnormal situation detection may be performed by components
associated with the abnormal situation prevention system 35
including blocks located within field devices. While FIG. 2
illustrates communications between the abnormal situation
prevention system applications 38, 40 and 42 and the database 43
and one or more data collection and processing blocks within HART
and Fieldbus field devices, it will be understood that similar
communications can occur between the abnormal situation prevention
system applications 38, 40 and 42 and other devices and equipment
within the process plant 10, including any of the devices and
equipment illustrated in FIG. 1.
[0040] The portion 50 of the process plant 10 illustrated in FIG. 2
includes a distributed process control system 54 having one or more
process controllers 60 connected to one or more field devices 64
and 66 via input/output (I/O) cards or devices 68 and 70, which may
be any desired types of I/O devices conforming to any desired
communication or controller protocol. The field devices 64 are
illustrated as HART field devices and the field devices 66 are
illustrated as Fieldbus field devices, although these field devices
could use any other desired communication protocols. Additionally,
the field devices 64 and 66 may be any types of devices such as,
for example, sensors, valves, transmitters, positioners, etc., and
may conform to any desired open, proprietary or other communication
or programming protocol, it being understood that the I/O devices
68 and 70 must be compatible with the desired protocol used by the
field devices 64 and 66.
[0041] In any event, one or more user interfaces or computers 72
and 74 (which may be any types of personal computers, workstations,
etc.) accessible by plant personnel such as configuration
engineers, process control operators, maintenance personnel, plant
managers, supervisors, etc. are coupled to the process controllers
60 via a communication line or bus 76 which may be implemented
using any desired hardwired or wireless communication structure,
and using any desired or suitable communication protocol such as,
for example, an Ethernet protocol. In addition, a database 78 may
be connected to the communication bus 76 to operate as a data
historian that collects and stores configuration information as
well as on-line process variable data, parameter data, status data,
and other data associated with the process controllers 60 and field
devices 64 and 66 within the process plant 10. Thus, the database
78 may operate as a configuration database to store the current
configuration, including process configuration modules, as well as
control configuration information for the process control system 54
as downloaded to and stored within the process controllers 60 and
the field devices 64 and 66. Likewise, the database 78 may store
historical abnormal situation prevention data, including
statistical data collected and/or generated by the field devices 64
and 66 within the process plant 10 or statistical data determined
from process variables collected by the field devices 64 and
66.
[0042] While the process controllers 60, I/O devices 68 and 70, and
field devices 64 and 66 are typically located down within and
distributed throughout the sometimes harsh plant environment, the
workstations 72 and 74, and the database 78 are usually located in
control rooms, maintenance rooms or other less harsh environments
easily accessible by operators, maintenance personnel, etc.
[0043] Generally speaking, the process controllers 60 store and
execute one or more controller applications that implement control
strategies using a number of different, independently executed,
control modules or blocks. The control modules may each be made up
of what are commonly referred to as function blocks, wherein each
function block is a part or a subroutine of an overall control
routine and operates in conjunction with other function blocks (via
communications called links) to implement process control loops
within the process plant 10. As is well known, function blocks,
which may be objects in an object-oriented programming protocol,
typically perform one of an input function, such as that associated
with a transmitter, a sensor or other process parameter measurement
device, a control function, such as that associated with a control
routine that performs PID, fuzzy logic, etc. control, or an output
function, which controls the operation of some device, such as a
valve, to perform some physical function within the process plant
10. Of course, hybrid and other types of complex function blocks
exist, such as model predictive controllers (MPCs), optimizers,
etc. It is to be understood that while the Fieldbus protocol and
the DeltaV.TM. system protocol use control modules and function
blocks designed and implemented in an object-oriented programming
protocol, the control modules may be designed using any desired
control programming scheme including, for example, sequential
function blocks, ladder logic, etc., and are not limited to being
designed using function blocks or any other particular programming
technique.
[0044] As illustrated in FIG. 2, the maintenance workstation 74
includes a processor 74A, a memory 74B and a display device 74C.
The memory 74B stores the abnormal situation prevention
applications 38, 40 and 42 discussed with respect to FIG. 1 in a
manner that these applications can be implemented on the processor
74A to provide information to a user via the display 74C (or any
other display device, such as a printer).
[0045] Additionally, as shown in FIG. 2, some (and potentially all)
of the field devices 64 and 66 include data collection and
processing blocks 80 and 82. While, the blocks 80 and 82 are
described with respect to FIG. 2 as being advanced diagnostics
blocks (ADBs), which are known Foundation Fieldbus function blocks
that can be added to Fieldbus devices to collect and process
statistical data within Fieldbus devices, for the purpose of this
discussion, the blocks 80 and 82 could be or could include any
other type of block or module located within a process device that
collects device data and calculates or determines one or more
statistical measures or parameters for that data, whether or not
these blocks are located in Fieldbus devices or conform to the
Fieldbus protocol. While the blocks 80 and 82 of FIG. 2 are
illustrated as being located in one of the devices 64 and in one of
the devices 66, these or similar blocks could be located in any
number of the field devices 64 and 66, could be located in other
devices, such as the controller 60, the I/O devices 68, 70, in an
intermediate device that is located within the plant and that
communicates with multiple sensors or transmitters and with the
controller 60, or any of the devices illustrated in FIG. 1.
Additionally, the blocks 80 and 82 could be in any subset of the
devices 64 and 66.
[0046] Generally speaking, the blocks 80 and 82 or sub-elements of
these blocks, collect data, such a process variable data, within
the device in which they are located and perform statistical
processing or analysis on the data for any number of reasons. For
example, the block 80, which is illustrated as being associated
with a valve, may have a stuck valve detection routine which
analyzes the valve process variable data to determine if the valve
is in a stuck condition. In addition, the block 80 includes a set
of four statistical process monitoring (SPM) blocks or units
SPM1-SPM4 which may collect process variable or other data within
the valve and perform one or more statistical calculations on the
collected data to determine, for example, a mean, a median, a
standard deviation, a root-mean-square (RMS), a rate of change, a
range, a minimum, a maximum, etc. of the collected data and/or to
detect events such as drift, bias, noise, spikes, etc., in the
collected data. Neither the specific statistical data generated,
nor the method in which it is generated is critical. Thus,
different types of statistical data can be generated in addition
to, or instead of, the specific types described above.
Additionally, a variety of techniques, including known techniques,
can be used to generate such data. The term statistical process
monitoring (SPM) block is used herein to describe functionality
that performs statistical process monitoring on at least one
process variable or other process parameter, and may be performed
by any desired software, firmware or hardware within the device or
even outside of a device for which data is collected. It will be
understood that, because the SPMs are generally located in the
devices where the device data is collected, the SPMs can acquire
quantitatively and qualitatively more accurate process variable
data. As a result, the SPM blocks are generally capable of
determining better statistical calculations with respect to the
collected process variable data than a block located outside of the
device in which the process variable data is collected.
[0047] As another example, the block 82 of FIG. 2, which is
illustrated as being associated with a transmitter, may have a
plugged line detection unit that analyzes the process variable data
collected by the transmitter to determine if a line within the
plant is plugged. In addition, the block 82 includes a set of four
SPM blocks or units SPM1-SPM4 which may collect process variable or
other data within the transmitter and perform one or more
statistical calculations on the collected data to determine, for
example, a mean, a median, a standard deviation, etc. of the
collected data. If desired, the underlying operation of the blocks
80 and 82 may be performed or implemented as described in U.S. Pat.
No. 6,017,143 referred to above. While the blocks 80 and 82 are
illustrated as including four SPM blocks each, the blocks 80 and 82
could have any other number of SPM blocks therein for collecting
data and determining statistical measures associated with that
data. Likewise, while the blocks 80 and 82 are illustrated as
including detection software for detecting particular conditions
within the plant 10, they need not have such detection software or
could include detection software for detecting other conditions
within the plant as described below. Still further, while the SPM
blocks discussed herein are illustrated as being sub-elements of
ADBs, they may instead be stand-alone blocks located within a
device. Also, while the SPM blocks discussed herein may be known
Foundation Fieldbus SPM blocks, the term statistical process
monitoring (SPM) block is used herein to refer to any type of block
or element that collects data, such as process variable data, and
performs some statistical processing on this data to determine a
statistical measure, such as a mean, a standard deviation, etc. As
a result, this term is intended to cover software or firmware or
other elements that perform this function, whether these elements
are in the form of function blocks, or other types of blocks,
programs, routines or elements and whether or not these elements
conform to the Foundation Fieldbus protocol, or some other
protocol, such as PROFIBUS, WORLDFIP, Device-Net, AS-Interface,
HART, CAN, etc., protocols.
[0048] FIG. 3 illustrates a block diagram of an SPM block 90 (which
could be any of the SPM blocks in the blocks 80 and 82 of FIG. 2)
which accepts raw data on an input 92 and operates to calculate
various statistical measures of that data, including a Mean, an RMS
value, and one or more standard deviations. For a given set of raw
input data, the block 90 may also determine a minimum value (Min),
a maximum value (Max) and a range. If desired, this block may
calculate specific points within the data, such as the Q25, Q50 and
Q75 points and may perform outliner removal based on the
distributions. Of course this statistical processing can be
performed using any desired or known processing techniques.
[0049] Referring again to FIG. 2, in one embodiment, each SPM block
within the ADBs 80 and 82 can be either active or inactive. An
active SPM block is one that is currently monitoring a process
variable (or other process parameter) while an inactive SPM block
is one that is not currently monitoring a process variable.
Generally speaking, SPM blocks are, by default, inactive and,
therefore, each one must generally be individually configured to
monitor a process variable. FIG. 4 illustrates an example
configuration display 84 that may be presented to a user, engineer,
etc. to depict and change the current SPM configuration for a
device. As indicated in the display 84, SPM blocks 1, 2 and 3 for
this particular device have all been configured, while SPM block 4
has not been configured. Each of the configured SPM blocks SPM1,
SPM2 and SPM3 is associated with a particular block within a device
(as indicated by the block tag), a block type, a parameter index
within the block (i.e., the parameter being monitored) and a user
command which indicates the monitoring functionality of the SPM
block. Still further, each configured SPM block includes a set of
thresholds to which determined statistical parameters are to be
compared, including for example, a mean limit, a high variation
limit (which specifies a value that indicates too much variation in
the signal) and low dynamics (which specifies a value that
indicates too little variation in the signal). Essentially,
detecting a change in a mean may indicate that the process is
drifting up or down, detecting a high variation may mean that an
element within the process is experiencing unexpected noise (such
as that caused by increased vibration) and detecting a low
variation may mean that a process signal is getting filtered or
that an element is getting suspiciously quiet, like a stuck valve
for example. Still further, baseline values, such as a mean and a
standard deviation may be set for each SPM block. These baseline
values may be used to determine whether limits have been met or
exceeded within the device. SPM blocks 1 and 3 of FIG. 4 are both
active because they have received user commands to start
monitoring. On the other hand, SPM block 2 is inactive because it
is in the Idle state. Also, in this example SPM capabilities are
enabled for the entire device as indicated by the box 86 and are
set to be monitored or calculated every five minutes, as indicated
by the box 88. Of course, an authorized user could reconfigure the
SPM blocks within the device to monitor other blocks, such as other
function blocks, within the device, other parameters associated
with these or other blocks within the device, as well as to have
other thresholds, baseline values, etc.
[0050] While certain statistical monitoring blocks are illustrated
in FIGS. 2 and 4, it will be understood that other parameters could
be monitored as well or in addition. For example, the SPM blocks,
or the ADBs discussed with respect to FIG. 2 may calculate
statistical parameters associated with a process and may trigger
certain alerts, based on changes in these values. By way of
example, Fieldbus type SPM blocks may monitor process variables and
provide 15 different parameters associated with that monitoring.
These parameters include Block Tag, Block Type, Mean, Standard
Deviation, Mean Change, Standard Deviation Change, Baseline Mean,
Baseline Standard Deviation, High Variation Limit, Low Dynamics
Limit, Mean Limit, Status, Parameter Index, Time Stamp and User
Command. The two most useful parameters are currently considered to
be the Mean and Standard Deviation. However, other SPM parameters
that are often useful are Baseline Mean, Baseline Standard
Deviation, Mean Change, Standard Deviation Change, and Status. Of
course, the SPM blocks could determine any other desired
statistical measures or parameters and could provide other
parameters associated with a particular block to a user or
requesting application. Thus, SPM blocks are not limited to the
ones discussed herein.
[0051] As will be understood, the parameters of the SPM blocks
(SPM1-SPM4) within the field devices may be made available to an
external client, such as to the workstation 74 through the bus or
communication network 76 and the controller 60. Additionally or in
the alternative, the parameters and other information gathered by
or generated by the SPM blocks (SPM1-SPM4) within the ADBs 80 and
82 may be made available to the workstation 74 through, for
example, an OPC server 89. This connection may be a wireless
connection, a hardwired connection, an intermittent connection
(such as one that uses one or more handheld devices) or any other
desired communication connection using any desired or appropriate
communication protocol. Of course, any of the communication
connections described herein may use an OPC communication server to
integrate data received from different types of devices in a common
or consistent format.
[0052] Still further, it is possible to place SPM blocks in host
devices, other devices other than field devices, or other field
devices to perform statistical process monitoring outside of the
device that collects or generates the raw data, such as the raw
process variable data. Thus, for example, the application 38 of
FIG. 2 may include one or more SPM blocks which collect raw process
variable data via, for example, the OPC server 89 and which
calculate some statistical measure or parameter, such as a mean, a
standard deviation, etc. for that process variable data. While
these SPM blocks are not located in the device which collects the
data and, therefore, are generally not able to collect as much
process variable data to perform the statistical calculations due
to the communication requirements for this data, these blocks are
helpful in determining statistical parameters for devices or
process variable within devices that do not have or support SPM
functionality. Additionally, available throughput of networks may
increase over time as technology improves, and SPM blocks not
located in the device which collects the raw data may be able to
collect more process variable data to perform the statistical
calculations. Thus, it will be understood in the discussion below,
that any statistical measurements or parameters described to be
generated by SPM blocks, may be generated by SPM blocks such as the
SPM1-SPM4 blocks in the ADBs 80 and 82, or in SPM blocks within a
host or other devices including other field devices. Moreover,
abnormal situation detection and other data processing may be
performed using the statistical measures in the field devices or
other devices in which the SPM blocks are located, and thus
detection based on the statistical measures produced by the SPM
blocks is not limited to detection performed in host devices, such
as user interfaces.
[0053] Importantly, the maximum beneficial use of raw statistical
data and the calculation of various statistical measures based on
this data as described above is dependent in large part on the
accuracy of the raw or collected data in the first place. A number
of data processing functions or methods may be applied in the SPM
blocks to increase the accuracy or usefulness of the raw data
and/or to preprocess the raw data and develop more accurate or
better statistical data in the SPM blocks. These data processing
functions may be applied to massage or process raw field data prior
to exposing the raw or processed data to other field devices and
host systems. Moreover, in some cases, these data processing
functions may be used to provide diagnostics on the processed data
or on the raw data to generate alarms and/or warnings to users,
other field devices and host systems. The below described data
processing functions and methodologies are applicable to all
communication protocols such as HART, Fieldbus, Profibus, etc. and
are applicable to all field devices such as transmitters,
controllers, actuators, etc.
[0054] As will be understood, performing statistical and digital
signal processing within a field device provides the capability to
operate on the raw measurement data before any measurement and
control related modifications are made in the plant using the raw
data. Therefore, the signatures computed within a device are the
best indicators of the state of the sensing system, the mechanical
equipment and the process in which the device is installed. For
most communication systems, raw data collected at a high sampling
rate cannot be passed to a host system on a plant-wide basis due to
bandwidth limitations of the communication protocols between field
devices and the host system. Even if it becomes possible in the
future, loading the networks with excessive raw data transfers will
adversely affect the other tasks on the networks for measurement
and control. Thus, it is proposed in the first instance to provide
one or more data processing methodologies described herein within
SPM blocks or modules within the field devices or other devices
which collect the raw data.
[0055] As noted above, FIG. 3 illustrates a basic SPM block for
performing statistical process monitoring calculations on raw data.
As an example, the Rosemount 3051 transmitters use a simpler
version of the block of FIG. 3, where only the mean and the
standard deviation are computed and are passed to a host system.
However, it has been determined that calculating these values as
well as the RMS value and Range information of a signal does not
necessarily yield healthy monitoring and diagnostics information in
all cases. In fact, it has been found that in some cases, better
statistics may be determined by comparing these parameters not only
to their past baselines, but also to similar parameters evaluated
on processed forms of the raw data input. In particular, additional
information may be obtained by having the SPM block calculate
statistical measures of the raw data as well as statistical
measures of filtered or processed versions of the raw data and then
comparing these calculated statistical measures. As illustrated in
FIG. 5 for example, an SPM module 100 may include two SPM blocks
90a and 90b and a signal processing block 102. Raw data may be
processed as usual in the SPM block 90a to produce various
statistical measures (e.g., Min, Max, Range, Mean, RMS, Standard
Deviation, etc.) on the raw data. However, the raw data may also be
processed in the signal processing block 102, which may filter the
raw data, trim the raw data to remover outliers, etc. The processed
raw data may then be provided to the SPM block 90b which determines
one or more statistical measures on the processed data. The raw
data statistical measures and the processed data statistical
measures may then be compared to one another to detect or determine
information about the raw data. Moreover, one or both of the raw
data statistical measures and the processed data statistical
measures may be used in subsequent processing to perform, for
example, abnormal situation detection.
[0056] Thus, as will be understood, the signal processing block 102
of FIG. 5 may implement various data processing techniques that are
extremely useful in performing monitoring and diagnostics within a
process plant that using statistical process monitoring. The first
of these techniques is the capability to trim raw data, which is
useful in detecting and then eliminating spikes, outliers and bad
data points so that these data points do not slew statistical
parameters. Trimming could be performed based on sorting and
removing certain top and bottom percentages of the data, as well as
using thresholds based on the standard deviation or some weighted
moving average. Trimmed points may be removed from the data
sequence, or an interpolation may be performed to replace outlier
data with an estimate of what that data should be based on other
data collected prior to and/or after that data.
[0057] Moreover, the signal processing block 102 may perform one or
more different types of filtering to process the raw data. FIG. 6
illustrates a signal processing block 102a which includes multiple
filters to enable a user or the person configuring the system to
select the desired type of filtering. In the block 102a of FIG. 6,
three digital filters which may be applied individually or in
combination to achieve good results in many applications, as well
as good performance in determining accurate statistical data, are
illustrated as a low pass filter 104, a high pass filter 105, and a
bandpass filter 106. Of course other types and numbers of filters
could be provided as well or instead of those illustrated in FIG.
6. Additionally, a no filter option or block 107 simply passes data
unprocessed through the block 102a, while an off block 108 blocks
data through the block 102a. During configuration of the block
102a, a user may select the one or more filters 104-108 which are
to be used to filter the data in the processing block 102a. Of
course, the filters may be implemented using any known or available
digital signal processing techniques and may be specified or
defined using any known filter parameters, for example, the desired
slope of the filter, the pass and rejection frequencies of the
filter, etc.
[0058] FIG. 7 illustrates another signal processing block 102b that
can be used to filter and/or trim raw data. The signal processing
block 102b includes multiple standard filters (which may be for
example, low pass, high pass and band pass filters) 110 as well as
a custom filter 112. These options enable a user to select any of a
number of different desired filter characteristics within the
processing block 102b. Data trimming blocks 115 may be placed
before and/or after each of the filters 110 and 112 to perform data
trimming in any of the manners discussed above or using any known
or available technique. As will be understood, the data processing
block 102b enables a user or operator to select between one or more
standard filters to filter (and trim) the raw data as well as a
custom filter to filter (and trim) the raw data to produce filtered
(and trimmed) data. This configuration of a filtering and trimming
data to be provided to an SPM block provides a strong and versatile
technology that can be used in a broad spectrum of monitoring and
diagnostics applications.
[0059] Of course, many different types of filters may be used in
the SPM modules and data processing blocks such as those of FIGS.
5-7. In one embodiment, it is possible to isolate the noise portion
of a signal using one or more digital high pass IIR (infinite
impulse response) filters or FIR (finite impulse response) filters.
A typical FIR filter of order n has the following structure:
y t = i = 0 n a i * x t - i ##EQU00001##
where y is the filtered value, x is the current/previous
measurement and a is the filter coefficient. As is known, these
filters are designed to match certain frequency response criteria
to match a desired filter transfer function.
[0060] FIR filters are known and are currently used in, for
example, a known plugged line diagnostics algorithm provided in
known Rosemount transmitters and in the Rosemount AMS SNAP-ON
products. In these cases, the FIR filter is in the form of a
16.sup.th order FIR filter with the transfer function illustrated
in FIG. 8. In this figure, frequency is normalized so that 1 is
equal to the half the sampling rate which is 1 Hz. Therefore, as
illustrated in FIG. 8, the displayed filter will block all parts of
the signal from DC to about 1.1 Hz and will pass the parts from
about 3.3 Hz to 11 Hz. The transition band is from about 1.1 Hz to
about 3.3 Hz. The primary purpose of this filter is to remove
transients from the signal so that it is possible to compute the
standard deviation of the noise. However, this filter can not
guarantee that all transients will be removed because some
transients will have faster components (i.e., falling with the pass
band of the filter). Unfortunately, it is not possible to design a
transition band much higher than that shown in FIG. 8 using FIR
techniques because such a transition band would filter process
noise along with transients. Thus, in summary, such an FIR filter
will either pass some transients or filter out some noise. In
addition, because the DC gain will not be zero, the mean of the
filtered signal will not reach zero, but will instead carry an
offset, which is not desirable. Furthermore, because this filter is
a 16.sup.th order filter, it requires many computations at every
point, which increases the required processing power and/or
decreases the ability to perform the filtering in real time,
especially when using a high sampling rate.
[0061] Another filter, which may be for example implemented as the
custom filter 112 of FIG. 7 and that can be advantageously used in
an SPM block or module for any purpose, for example to perform
plugged line diagnostics and flame instability detection, is a
simple difference filter. This difference filter can be pre-applied
to a data measurement sequence (e.g., prior to SPM block
processing) to evaluate and eliminate or reduce the short term
variation in the measurement sequence or signal. In particular,
this proposed difference filter, which again may be used to remove
trends/transients and to isolate the noise portion of a signal, may
be implemented, in one embodiment, as a first order difference
filter defined as:
y.sub.t=x.sub.t-x.sub.t-1
wherein: y.sub.t is the filtered output at time t, and x.sub.t is
the raw data at time t.
[0062] Of course, higher order difference filters may be used as
well or instead. The frequency response or transfer function of
this filter is illustrated in FIG. 9 and, as will be understood,
this filter continuously promotes higher frequencies and
continuously demotes lower frequencies. Because the frequency
content of the trends and transients in a signal are unknown, this
filter is believed to have an optimal structure for all possible
trends in a signal. As an example of the application of this
filter, FIG. 10 illustrates a pressure signal 120, composed of
signal trend and some pressure noise, while FIG. 11 illustrates the
filtered signal 122 after application of the proposed first order
difference filter described above (i.e., with the transfer function
shown in FIG. 9). It can be clearly seen from these results that a
difference filter can handle a variety of pressure conditions with
minimal computations.
[0063] The primary advantage of the difference filter described
above is that it removes intermediate and long term variations in a
given signal, and that it isolates the short term variation in the
signal, which is sometimes called the "process noise." Another
advantage of this difference filter is that it is a first order
filter and requires only one subtraction per measurement point, as
compared to 17 multiplications and 16 additions needed by the
16.sup.th order FIR filter described above. This difference filter
is therefore extremely computationally efficient and is thus
well-suited for on-board applications, i.e., those provided within
field devices and SPM blocks or modules located in the devices
within the process plant.
[0064] Another important aspect of making accurate and useful
statistical determinations in SPM blocks (and elsewhere) involves
selecting an appropriate data block or time length over which to
calculate the statistical measures, such as the mean, the standard
deviation, etc. In fact, an inherent problem in calculating the
mean, standard deviation, etc. for a given data sequence, is that
these statistical parameters depend heavily on the length of the
time period and thus the number of data points used to perform the
calculations. Using pure statistical guidelines for the number of
points as an appropriate sample set often does not work well
because most processes do not fit the underlying statistical
assumptions exactly, and thus the number of steady state points
suggested by these guidelines may not be available at any
particular time.
[0065] One method of calculating an appropriate block length to
use, however, includes collecting, during a test period, a number
of test points for a signal, wherein the number of test points is
much greater than the possible block length, determining the
frequency components (e.g., frequency domain) of the signal based
on the collected test points, determining the dominant system time
constant from the frequency components and then setting the block
length as some multiple (which may be an integer or a non-integer
multiple) of the dominant system time constant.
[0066] According to this method, the frequency components or domain
of a signal X(t) is first determined. For example, assume that the
data sequence in the time domain is given by X(t)=x.sub.1, x.sub.2,
x.sub.3, . . . x.sub.n, wherein the x data points are measured at
times t.sub.1, t.sub.2, t.sub.3, . . . t.sub.n. Here, it is assumed
that the corresponding time points t are uniformly spaced. The time
domain representation of a typical pressure signal 130 is depicted
in FIG. 12. Next, a Fourier Transform, such as a Fast Fourier
Transform may be applied to the pressure signal 130 to determine
the frequency components of the pressure signal 130. An example
transformed signal X(f) illustrating the frequency domain of X(t)
for the pressure signal 130 of FIG. 12 is illustrated as the plot
132 in FIG. 13. As is known, the FFT 132 of the signal X(t),
illustrates all of the cyclic behavior in the data as a function of
cyclic frequencies.
[0067] Next, a corner frequency f.sub.c of the pressure signal may
be determined by (1) finding the frequency where the FFT drops to
some factor (such as a factor of 10) from its peak and (2) finding
any isolated peaks in the FFT. In particular, it is desirable to
eliminate isolated peaks in the FFT prior to determining the
frequency drop because these peaks can pull the maximum FFT values
artificially high. That is, the corner frequency should be
determined based on the drop from the low frequency level of the
FFT after ignoring the isolated peaks or spikes in the FFT. Using
the isolated peaks in the FFT might lead to errors in the corner
frequency (or bandwidth) computations. Thus, in the plot of FIG.
13, the corner frequency f.sub.c may be selected as being
approximately 10 Hz. The corner frequency f.sub.c may then be used
to develop or estimate the dominant system time constant T.sub.C.
In one embodiment T.sub.C=1/f.sub.c.
[0068] A robust block size may then be chosen as some multiple of
the dominant system time constant T.sub.c. For example, ten times
the dominant system time constant T.sub.c may be used to produce a
robust block size for any application. However, other integer or
non-integer multiples of the dominant system time constant T.sub.c
may be used instead.
[0069] In some situations, it is desirable to fit or match a sine
wave to a specific data set to determine a best fit for a sine wave
to the data set, with the sine wave providing information about
specifics of the data set, such as dominant periodic frequency,
etc. One method that may be used to fit a sine wave to a given data
set is through the use of a linear least squares technique.
However, because the form of a sine wave is nonlinear, routine
linear regression methods cannot be applied to find the sine wave
parameters, and thus nonlinear curve fitting techniques have to be
applied to evaluate the parameters. However, nonlinear curve
fitting techniques typically require an excessive number of
iterative computations, which requires significant processing time
and power. Moreover, nonlinear techniques have to assure
computational stability and convergence to a solution, which are
highly complex concepts and hard to implement in SPM blocks or
modules.
[0070] To overcome these problems, two practical manners of fitting
a sine wave to a data set using a simple linear regression
technique, but that can be used in SPM blocks or other blocks
within field devices without requiring a lot of processing power
are described below.
[0071] As is known, a generic sine wave may be expressed in the
form of:
y(t)=a+b sin(.omega.t+.phi.)
and for this discussion, this will be the form of a sine wave to be
fitted. However, other sine wave forms may be used instead.
[0072] According to a first method of fitting this sine wave,
referred to herein as a one pass fit method, the sine wave
parameters a (the offset) and b (the gain) are first estimated
using simple techniques. For example, the offset a may be estimated
as the mean value of the entire data set while the gain b may be
estimated as half of the difference between a minimum and a maximum
value of the entire data set. Of course, the offset a may be
estimated using, for example, the median or other statistical
measure and the gain b may be estimated using some other technique,
such as using the root mean squared (RMS) value, etc.
[0073] Next, a variable transformation may be applied or selected
as:
z = Sin - 1 ( y ) - a b ##EQU00002##
where y is the measured data point. With this transformation, the
regression expression (the original sine wave form becomes:
z(t)=.omega.t+.phi.
[0074] This equation is obviously in a linear form and, as a
result, simple linear regression expressions can be used to fit
.omega. and .phi. as a function of time, resulting in an estimate
for each of the parameters of the sine wave (i.e., a, b, .omega.
and .phi.). In particular, the variable transformation defining z
is used to compute the transformed data points z(t) for each time
t. Then linear regression techniques can be used to select the
.omega. and .phi. that best fit the set of data points z(t).
[0075] A second method, referred to herein as an iterative fit
method, uses an iterative technique to determine the sine wave
parameters of a, b, .omega. and .phi.. In this method, the initial
values for a, b, .omega. and .phi. may be estimated using the
technique of the one pass fit method described above. Next, the
following variable transformation may be applied.
x=sin(.omega.t+.phi.)
With this transformation, the original sine wave expression (to be
fit) becomes:
y(x)=a+bx.
[0076] This equation is in a linear form and therefore simple
linear regression expressions can be used to fit a and b. These
parameters may then be used along with the variable transformation
defining x to fit for the parameters .omega. and .phi.. These
iterations may be executed until one or all four of the parameters
(a, b, .omega. and .phi.) converge, that is where:
|a.sub.k-a.sub.k-1|<.epsilon..sub.a
|b.sub.k-b.sub.k-1|<.epsilon..sub.b
|.omega..sub.k-.omega..sub.k-1|<.epsilon..sub..omega.
|.phi..sub.k-.phi..sub.k-1|<.epsilon..sub..phi.
Where k is the iteration step and .epsilon. is the desired
tolerance. The above convergence criteria are absolute with respect
to the parameters. However, if desired, a relative measure in
percent may also be employed for the parameters.
[0077] The first method outlined above provides an extremely fast
one pass fit for a function of sinusoidal shape using a linear
least squares fit. The second method combined with the first
method, on the other hand, while requiring more calculations,
typically provides a fit of the parameters to a desired accuracy
with only a couple of iterations. However, both methods are
extremely computationally efficient as compared to their nonlinear
counterparts, which results in significant savings in processing,
memory and storage requirements, making these methods more suitable
for a variety of fitting applications within SPM blocks.
[0078] One advantageous manner of using an SPM block relates to the
monitoring of a distillation column tray and performing diagnostics
using statistical process monitoring for the distillation column
tray. In particular, various diagnostics methodologies based on
actual pressure and differential pressure readings can be used to
determine the health of distillation columns (also called
fractionators). The distillation column is probably one of the most
important units in most refineries and chemical plants, because the
distillation column is responsible for most of the physical
separation processes in these plants. The methodologies described
here could be implemented either in the field devices within the
plant (in for example, a Rosemount 3426 transmitter), or at the
host system as software. The main advantage of these methods is the
use of statistical process parameters that are evaluated by field
instruments but that provide high quality measurements and faster
estimates.
[0079] FIG. 14 illustrates a schematic of a typical distillation
column 150 found in many refineries or chemical plants. As can be
see from FIG. 14, the distillation column 150 includes a
fractionator 152 into which the feed is applied. At the bottom of
the fractionator 152, the heavy fluid or "bottoms" material is
removed through a valve 154, which may be controlled based on a
level sensor 156 and a flow sensor or transmitter 158. Some of the
bottoms material is reheated in a reboiler 160 and provided back
into the fractionator 152 for further processing. At the top of the
fractionator 152, vapor is collected and is provided to a condenser
162 which condenses the vapor and supplies the condensed liquid to
a reflux drum 164. Gas in the reflux drum 164 may be removed
through a valve 166 based on a pressure sensor 168. Likewise, some
of the condensed liquid in the reflux drum 164 is proved out as
distillate through a valve 170 based on the measurements of a level
sensor. In a similar manner, some of the condensed liquid in the
reflux drum 164 is provided back into the fractionator 152 through
a valve 174 which may be controlled using flow and temperature
measurements from flow a sensor 176 and a temperature sensor
178.
[0080] FIG. 15. illustrates a schematic of a typical fractionator
152 used in petroleum processing showing the locations of various
trays that are sometimes used to extract liquids at various
physical condensation points. As illustrated in FIG. 15, flashed
crude is injected at tray 5 while heavy diesel is removed at tray
6, light diesel is removed at tray 13 and kerosene is removed at
tray 21. Preflashed gas and preflashed liquids may be injected at
trays 27 and 30. While the following discussion of the diagnostic
methods used in the distillation column refers to the trays of FIG.
15 as a baseline distillation column configuration, these methods
may be used in other distillation columns having other tray
arraignments and structures.
[0081] The first processing method determines if there is a low
pressure drop across two trays of the column. In particular, if the
pressure drop across a tray is less than a low nominal pressure, it
typically means that the tray is either damaged or is dumping. This
nominal low pressure (P.sub.ln) is, in one instance, 0.06 psi
(pounds per square inch) for a 24 inch diameter (D.sub.n)
distillation tray. For other sizes of tray diameters (D) the
nominal low pressure P.sub.1 may be calculated as:
P l = P ln D D n ##EQU00003##
[0082] Statistical process monitoring can be used to determine a
baseline for the pressure drop across a tray using any of the SPM
blocks and techniques described above, and then a monitoring phase
may be used in an SPM or other block to detect the reduction in the
mean pressure drop. If the differential pressure is measured across
multiple trays, the expected pressure drop is simply the pressure
drop for a single tray times the number of trays. Thus, after
determining a baseline pressure drop across a tray for the
fractionator 152 of FIG. 15 using pressure sensors (not shown in
FIG. 15) at the appropriate locations within the fractionator 152
or using a threshold established using the low nominal pressure
calculations discussed above, SPM blocks may monitor the pressures
to determine a mean pressure at each location and to determine the
difference between these mean pressures. If the difference becomes
lower then the low nominal pressure (set as a threshold), then an
alarm or alert may be sent indicating that the tray is damages or
is dumping, or is at a condition that it will start this
process.
[0083] Additionally, a high pressure drop across trays of a
distillation column may be determined using this same technique. In
particular, if the pressure drop across ahoy is more than a high
nominal pressure, it typically indicates that either there is
fouling or there is plugging (e.g., at least partial plugging) of
the tray. The nominal high pressure (P.sub.hn,) may be 0.12 psi for
a 24 inch diameter (D.sub.n) distillation tray. For other sizes of
trays, the P.sub.h may be calculated as:
P h = P hn D D n ##EQU00004##
[0084] Similar to the low pressure drop method described above,
statistical process monitoring can be used to determine a baseline
mean pressure drop across a tray or a group of trays or a threshold
may be established using the calculations described above, and then
the monitoring phase is used to detect the reduction in the mean
pressure drop. If the differential pressure is measured across
multiple trays, the expected pressure drop is simply the pressure
drop for a single tray times the number of trays. In either case,
it will be understood that distillation column pressure drop
monitoring using statistical parameters provides a fast and
efficient indication of tray problems in chemical and refining
industries.
[0085] Additionally, diagnostics using statistical process
monitoring may be advantageously performed in fluid catalytic
crackers (FCCs). In particular, various diagnostic methodologies
can be used to determine the health of an FCC, which is highly
advantageous because the FCC is probably the most important unit in
a refinery, as it is responsible for most of production of gasoline
in a refinery, which is typically the most important and most
prevalent product produced by the refinery. The statistical
processing methodologies described here can be implemented either
in field devices, such as in the Rosemount 3420 transmitter, or at
the host system as software. The main advantage of these methods is
the use of statistical process parameters evaluated by field
instruments that provide high quality measurements and faster
estimates.
[0086] FIG. 16 illustrates a schematic of a typical FCC 200 found
in refineries and that will be used as the baseline FCC
configuration for the diagnostic methods described herein. However,
it will be understood that these methodologies may be used in other
types of FCCs or in FCCs with other configurations as well. In
particular, as illustrated in FIG. 16, the FCC 200 includes a
reactor 202 and a catalyst regenerator 204. During operation, feed
and dispersion steam are feed into a riser 206 where the feed
reacts with regenerated catalyst. This process "cracks" the feed.
At the top of the reactor 202, the product and catalyst are
separated with the product being expelled as reactor effluent. The
catalyst falls to the bottom of the reactor 202 and is steam
stripped using stripping steam. The spent catalyst is then provided
through a pipe 206 controlled by a valve 208 to the regenerator
204. The spent catalyst is input into a combustion chamber and is
mixed with superheated air provided by an air blower 212 which
burns the coke that has formed on the catalyst as a result of the
catalytic reaction in the reactor 202. This process regenerates the
catalyst. The heat from this process and the regenerated catalyst
are then provided back to the bottom of the reactor 202 via a
regenerated catalyst pipe 220 controlled by a regenerated catalyst
valve 222 to mix with the incoming feed.
[0087] A first statistical method may be used in the FCC 200 to
detect a failed or faulty air compressor or blower. In particular,
a failed air compressor results in a reversal of flow in the
regenerated catalyst pipe 220 resulting in flow from the reactor
202 to the regenerator 204. This condition may be detected by
monitoring pressure in the regenerator 204 or monitoring
differential pressure across the regenerated catalyst valve 222. In
particular, during normal operation of the FCC 200, the pressure in
the regenerator 204 is higher than that in the reactor or riser
pipe 202, which produces the flow of regenerated catalyst in the
correct direction. Loss of the compressor 212 on the regenerator
204 causes a loss of pressure at the regenerator 204 and results in
a reversal of this differential pressure.
[0088] Additionally, a statistical method may be used to detect
reactor to regenerator pipe plugging. In particular, when the pipe
206 between the reactor 202 and the regenerator 204 plugs, the
reactor 202 fills with catalyst and the catalyst enters into the
exhaust or reactor effluent. This condition may be detected by
monitoring the mean catalyst level in the reactor 202 using, for
example, a level sensor/transmitter 224 as plugging in the pipe 202
causes the catalyst level in the reactor 202 to rise. With proper
catalyst level baselining, detecting the mean level of the catalyst
within the reactor 202 and comparing it to a baseline mean level
for the catalyst could be used to detect plugging in the pipe 206.
A second indication that may be used to determine plugging of the
pipe 202 may be based on the cross correlation between the
pressures and levels in the reactor 202 and the regenerator 204, as
the plugging of the pipe 206 would change this correlation. That
is, a baseline cross correlation of the mean pressures and levels
in the reactor 202 and the regenerator 206 may be determined and
then across correlation between these pressures and levels (or the
means or other statistical measures of these pressures and levels)
may be periodically determined and compared to the baseline, with a
significant change in the cross-correlation indicating a potential
plugging of the pipe 206.
[0089] Moreover, a statistical method may be used to detect a
catalyst flow problem or a flow instability in the reactor 202. In
particular, a catalyst flow instability will result in a bad
product quality and in the catalyst entering into the exhaust of
the reactor 202. This condition may be detected using the standard
deviation of the differential pressure across the regenerated
catalyst valve 222, it being understood that a flow instability
would cause an increase in the standard deviation of the
differential pressure across the catalyst valve 222.
[0090] A statistical method may also be used to detect if there is
insufficient steam flow into the reactor 202, which typically
results in thermal cracking and coke formation. In particular,
detecting insufficient steam flow and correcting the problem
reduces catalytic cracking and gives rise to thermal cracking. The
existence of insufficient steam flow can be detected by monitoring
the mean temperature in the reactor 202. In particular, an increase
in mean the reactor temperature indicates a insufficient steam flow
problem.
[0091] A statistical method may also be used to detect an extreme
thermal distribution in the reactor 202, which leads to the
formation of coke and therefore fouling of the reactor 202. Extreme
thermal distribution may be detected by measuring the reactor
temperature at multiple points in the reactor. Uneven temperatures
would cause certain regions in reactor 202 to become very hot,
which results in the formation of coke in the reactor. Monitoring
these temperatures and detecting regions that have very high or low
temperatures (or very high or low mean temperatures) as compared to
a baseline mean or a threshold yields diagnostics related to
extreme thermal distributions.
[0092] A statistical method may also be used to detect thermal
cracking in the exhaust pipe after the reactor 202, which again
leads to the formation of coke in this section of the FCC 200. This
condition may be detected by monitoring the mean temperature
difference between the exhaust pipe and the reactor vessel. If the
mean temperature difference becomes more than some threshold level,
such as three degrees Fahrenheit, there may be thermal cracking
occurring in the exhaust pipe.
[0093] There are three possible platforms to implement these
statistical methods and detection. In particular, these conditions
may be detected as part of a transmitter advanced diagnostics block
disposed within a valve or a transmitter within the FCC 200, such
as in the valve 222, the valve 208, a temperature
sensor/transmitter, a level sensor/transmitter, a pressure
sensor/transmitter, etc. In particular, this diagnostic block may
be trained to detect or determine a baseline pressure, temperature,
level, differential pressure, etc. when the system is healthy, and
then may monitor the mean value of the appropriate pressures,
temperatures, levels, differential pressures, etc. after
establishing the baseline. On the other hand, this monitoring and
detection could be achieved using an SPM block in a transmitter or
other field device with a simple threshold logic. That is, the SPM
block could monitor one or more process variables to determine the
mean, the standard deviation, etc. for these variables and compare
these statistical measures to a pre-established threshold (which
may be set by a user or which may be based on a baseline
statistical measure computed from measurements of the appropriate
process variables during a training period). Also, if desired, host
level software run in a user interface device or other computing
device connected to the field devices, such as an advanced
diagnostic block explorer or expert, maybe used to set and monitor
normal and abnormal pressures, temperatures, levels and
differential pressures and to perform abnormal situation detection
based on the concepts described above.
[0094] Some or all of the blocks, such as the SPM or ADB blocks
illustrated and described herein may be implemented in whole or in
part using software, firmware, or hardware. Similarly, the example
methods described herein may be implemented in whole or in part
using software, firmware, or hardware. If implemented, at least in
part, using a software program, the program may be configured for
execution by a processor and may be embodied in software
instructions stored on a tangible medium such as CD-ROM, a floppy
disk, a hard drive, a digital versatile disk (DVD), or a memory
associated with the processor. However, persons of ordinary skill
in the art will readily appreciate that the entire program or parts
thereof could alternatively be executed by a device other than a
processor, and/or embodied in firmware and/or dedicated hardware in
a well known manner.
[0095] While the invention is susceptible to various modifications
and alternative constructions, certain illustrative embodiments
thereof have been shown in the drawings and are described in detail
herein. It should be understood, however, that there is no
intention to limit the disclosure to the specific forms disclosed,
but on the contrary, the intention is to cover all modifications,
alternative constructions and equivalents falling within the spirit
and scope of the disclosure as defined by the appended claims.
* * * * *